Shuang Li
Assistant Professor and Presidential Young Fellow
School of Data Science
The Chinese University of Hong Kong (Shenzhen)
Daoyuan Building, 508b
Shenzhen, Guangdong, China



I am currently an Assistant Professor in the School of Data Science at The Chinese University of Hong Kong (Shenzhen) . I was a postdoctoral fellow with the Department of Statistics at Harvard University from Sep, 2019 to May, 2021. I was very fortunate to work with Prof. Susan Murphy in mobile health. I obtained my Ph.D. in Industrial Engineering (specialization in Statistics, minor in Operations Research) from H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech in summer 2019. I received B.E. in Automation from University of Science and Technology, China in 2011, and M.S. in Statistics from Georgia Tech in 2014.

I interned at Google in summer 2018, working on deep learning for user behavior modeling.

[Google Scholar] [Curriculum Vitae]

I am now recruiting Ph.d. students (2023), research assistants, and postdocs. I still have two Ph.d. opennings. If you are interested in my Ph.D. position starting in Fall 2023, you can contact me via email.

Research Interests

My research has yielded new sequential data analysis and decision-making tools, all inspired and motivated by applications in healthcare, smart cities and social media. I develop new methodological frameworks that combine deep learning, time series analysis and point processes to address the complexity and volume of the sequential data collected in modern systems.

More specifically, my research focuses on:

  • Novel models to capture complex dynamics in sequences

  • Reliable and efficient learning methods to uncover latent model parameters

  • Effective inference procedures based on these models to perform accurate prediction, reliable detection, and smart interventions

Highlights of my research can be found here.


Feb, 2023, I was invited to serve as an area chair at NeurIPs, 2023.

Jan, 2022, our paper "Explaining Point Processes by Learning Interpretable Temporal Logic Rules" was accepted by ICLR, 2022.

Dec, 2021, I was invited to serve as a meta reviewer (i.e., area chair) at ICML, 2022.

June, 2021, I became a tenure-track Assistant Professor with the School of Data Science at The Chinese University of Hong Kong (Shenzhen).





  • Temporal Logic Point Processes
    S. Li, L. Wang, R. Zhang, X. Chang, X. Liu, Y. Xie, Y. Qi, and L. Song
    International Conference on Machine Learning (ICML), 2020.


  • Detecting Weak Changes in Dynamic Events over Networks
    S. Li, Y. Xie, M. Farajtabar, A. Verma, and L. Song
    IEEE Transactions on Signal and Information Processing over Networks, Vol. 3, No. 2, June 2017.
           — Finalist of 2018 INFORMS Social Media analytics Best Student Paper Competition

Book Chapter




Program Committee/External Reviewer for:

  • Journal of American Statistical Association, Annals of Applied Statistics, IEEE Transactions on Signal Processing, IEEE Transactions on Information Theory, Transactions on Knowledge and Data Engineering, IEEE Transactions on Neural Networks and Learning Systems